13 citations · 20 across the 4 of their papers we have counts for
4 papers
Dive into Big Model Training
Qinghua Liu, Yuxiang Jiang
The increasing scale of model size and continuous improvement of performance herald the arrival of the Big Model era. In this report, we explore what and how the big model training…
Pseudo-Mallows for Efficient Probabilistic Preference Learning
Qinghua Liu, Valeria Vitelli, Carlo Mannino +2
We propose the Pseudo-Mallows distribution over the set of all permutations of items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has…
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Qinghua Liu, Andrew Henry Reiner, Arnoldo Frigessi +1
Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Curre…
BayesMallows: An R Package for the Bayesian Mallows Model
Øystein Sørensen, Marta Crispino, Qinghua Liu +1
BayesMallows is an R package for analyzing data in the form of rankings or preferences with the Mallows rank model, and its finite mixture extension, in a Bayesian probabilistic fr…